bids_bep16_conv.datasets.eval_HBN_qc

bids_bep16_conv.datasets.eval_HBN_qc(HBN_qc_file_df, n_high_participants=5, visualize=True, return_sorted_df=False)[source]

Evaluate QSIprep’s participants.tsv file obtained for the HBN dataset regarding QC.

Parameters:

  • HBN_qc_file_df (string or Pandas DataFrame) – Either a string indicating the path of the file or a corresponding Pandas DataFrame. will be saved.

  • n_high_participants (int) – Integer indicating how many of the participants with the best QC sure should be displayed.

  • visualize (bool) – Indicate if the distribution of QC scores should be displayed via a raincloud plot.

  • return_sorted_df (bool) – Indicate if the DataFrame should be sorted by QC scores in a descending manner and returned.

Returns:

  • HBN_qc_file_df_n_high (Pandas Series) – A Pandas Series indicating the participants with the highest QC scores with the amount being defined by n_high_participants.

  • HBN_qc_file_df (Pandas DataFrame) – The DataFrame, sorted by QC scores. Returned only if return_sorted_df is set to True.

  • fig (Figure) – A Figure containing a raincloud plot of the QC scores.

Examples

Get the participants with the 10 highest QC scores and do not show the plot nor return the sorted DataFrame.

>>> eval_HBN_qc(HBN_qc_file_df, n_high_participants=10, visualize=False, return_sorted_df=False)

Get the participants with the 3 highest QC scores and do show the plot and return the sorted DataFrame.

>>> eval_HBN_qc(HBN_qc_file_df, n_high_participants=3, visualize=True, return_sorted_df=True)